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Companies engage in several business relationships ranging from arm's length to close relationships based on trust and commitment. Several companies have recognized that their supply chain capabilities give them a competitive edge for delivering what customers want. However, often customers are not a homogeneous group requiring the same physical products and services. From a manufacturer's perspective, this demands that the issue of customer and supplier attractiveness should be considered. How can a company work with a differentiated approach: to be more attractive to selected customers or to suppliers? The purpose of this paper is to address this issue by proposing a process and maturity model for customer attractiveness in supply chains.

The paper is based on two in‐depth explorative case studies of Danish business‐to‐business manufacturers. The cases report both seller and buyer perspectives on attractiveness.

The literature review on attractiveness reveals that the explanation of attractiveness has been described differently by a range of authors who are divided into three levels. This challenge calls for the development of a maturity model. Based on the developed maturity model, different sets of managerial implications are deducted. It is found that the parameters used at different attractiveness stages have to differ both in scope and in actual usage.

The research is explorative in nature and rests on two case studies. This does not provide a basis for statistical generalization. Future research should test the maturity model, through both more case studies and more questionnaire surveys.

The maturity model helps companies manage their resources more efficiently. It provides a structured framework showing where to start and a foundation for an assessment of companies' current customer and supplier attractiveness.

The paper is the first to develop a maturity model for customer and supplier attractiveness from a supply chain management perspective.

While few would disagree that high data quality is a precondition for the efficiency of a company, this remains an area to which many companies do not give adequate attention. Thus, this paper aims to identify which are the most important barriers preventing companies from achieving high data quality. By improving awareness of barriers on which to concentrate, companies are put in a better position to achieve high quality data.

First, a literature review of data quality and data quality barriers is carried out. Based on this literature review, the paper identifies a set of overall barriers to ensuring high data quality. The significance of these barriers is investigated by a questionnaire study, which includes responses from 90 Danish companies. Because of the fundamental difference between master data and transaction data, the questionnaire is limited to focusing only on master data.

The results of the survey indicate that a lack of delegation of responsibilities for maintaining master data is the single aspect which has the largest impact on master data quality. Also, the survey shows that the vast majority of the companies believe that poor master data quality does have significant negative effects.

The contributions of this paper represent a step towards an improved understanding of how to increase the level of master data quality in companies. This knowledge may have a positive impact on the data quality in companies. However, since the study presented in this paper appears to be the first of its kind, the conclusions drawn need further investigation by other research studies in the future.

This paper identifies the main barriers for ensuring high master data quality and investigates which of these factors are the most important. By focusing on these barriers, companies will have better chances of increasing their data quality.

The study presented in this paper appears to be the first of its kind, and it represents an important step towards understanding better why companies find it difficult to achieve satisfactory data quality levels.

The development of IT has enabled organizations to collect and store many times more data than they were able to just decades ago. This means that companies are now faced with managing huge amounts of data, which represents new challenges in ensuring high data quality. The purpose of this paper is to identify barriers to obtaining high master data quality.

This paper defines relevant master data quality barriers and investigates their mutual importance through organizing data quality barriers identified in literature into a framework for analysis of data quality. The importance of the different classes of data quality barriers is investigated by a large questionnaire study, including answers from 787 Danish manufacturing companies.

Based on a literature review, the paper identifies 12 master data quality barriers. The relevance and completeness of this classification is investigated by a large questionnaire study, which also clarifies the mutual importance of the defined barriers and the differences in importance in small, medium, and large companies.

The defined classification of data quality barriers provides a point of departure for future research by pointing to relevant areas for investigation of data quality problems. The limitations of the study are that it focuses only on manufacturing companies and master data (i.e. not transaction data).

The classification of data quality barriers can give companies increased awareness of why they experience data quality problems. In addition, the paper suggests giving primary focus to organizational issues rather than perceiving poor data quality as an IT problem.

Compared to extant classifications of data quality barriers, the contribution of this paper represents a more detailed and complete picture of what the barriers are in relation to data quality. Furthermore, the presented classification has been investigated by a large questionnaire study, for which reason it is founded on a more solid empirical basis than existing classifications.

The purpose of this paper is to investigate the extent, drivers, and conditions underlying backshoring in the Finnish manufacturing industry, comparing the results to the wider ongoing relocation of production in the international context.

The survey of 229 Finnish manufacturing firms reveals the background, drivers, and patterns of offshoring and backshoring.

Companies that had transferred their production back to Finland were more commonly in industries with relatively higher technology intensity and they were typically larger than the no-movement companies, and with a higher number of plants. They also reported more commonly having a corporate-wide strategy for guiding production location decisions.

Backshoring activity in the small and open economy of Finland seems to be higher compared to earlier studies in larger countries. The findings suggest that there is a transformation in the manufacturing industries with some gradual replacement of labor-intensive and lower technology-intensive industries toward higher technology-intensive industries.

Moving production across national borders is one option in the strategies of firms to stay competitive. Companies must carefully consider the relevance of various decision-making drivers when determining strategies for their production networks.

Manufacturing industries have traditionally been important for employment in the relatively small and open economies of the Nordic countries. From the social perspective, it is important to understand the ongoing transformation and its implications.

There are few empirical studies available of the ongoing backshoring movement, utilizing data from company decision makers instead of macroeconomic factors.

Several studies have documented that information technology (IT) projects often do not successfully meet defined objectives regarding time, budget, and functionality. There can be multiple causes for this, and an important factor in this context is the extent to which a company is ready for an IT project. To help understand this dynamic, this paper seeks to present a framework for analyzing “IT readiness” in small‐ and medium‐sized enterprises (SMEs).

Based on a literature review, the paper defines a framework for assessing and changing the IT readiness of a SME. The framework is illustrated and investigated by three case studies.

The case studies show that the framework of IT readiness in SMEs is useful for assessing company readiness and supporting the management of a project.

The framework and case studies provide an improved understanding of how to evaluate the readiness of a SME for an IT project.

The framework for IT readiness provides a solid basis for SMEs who plan to engage in an IT project and help to increase the chances of success.

The framework presented in the paper constitutes an operational model for choosing IT projects and making SMEs ready to engage in IT projects.

Compared with the private sector, the public sector's procurement process differs in several respects. The purpose of this paper is to analyze the possibility for mutual learning and the value between the public and private sectors and also to identify both drivers and barriers for benchmarks between the two sectors.

The paper is based on in‐depth literature reviews of comparisons between private and public procurements. The paper is, furthermore, derived from two case studies: one in a chain perspective and another that concerns public‐private innovation.

Extant literature contains limited contributions that compare public procurement practice with private purchasing practice. Using tendering to regulate procurement is troublesome and may hamper the possibility to learn and gain value measured on a broader scale. Wider collaboration may provide more possibilities to learn and gain value.

The empirical part of the paper rests on two case studies. The procurement process of a single item has been studied as have new cooperation modes between the public and private sectors.

The paper provides supply chain management (staff) input as to examples in which comparisons of procurement and purchasing processes might add value. The paper argues that both sectors can learn from each other.

This paper is the first report about an in‐depth literature review of comparisons of public procurement with private purchase, and it is the first to empirically analyze a chain of relations from private‐private to private‐public. It further addresses new ways to perceive the EU Directive of public tendering.

In literature, there is not agreement on the relevant data quality dimensions in an enterprise resource planning (ERP) system context. The purpose of this paper is to provide some clarification of this topic, by answering two important questions: What are the most relevant dimensions for assessing ERP data quality? What are the causal relationships between these data quality dimensions?

Based on a discussion of existing literature on data quality, a classification model of ERP system data quality is proposed and the relationships between the defined categories of data quality dimensions are defined. The validity of the classification model and the relationships between categories of data quality dimensions are investigated in three case studies.

The three case studies confirm that the classification model captures the most important aspects of describing ERP data quality and that the defined causalities between categories of data quality dimensions correspond with practice.

Besides being relevant in an ERP system context, the contribution of this paper may also be applicable for the evaluation of data quality in other types of information systems.

The defined classification model of ERP system data quality may support companies in improving their ERP data quality, thereby achieving greater benefits from their ERP systems.

A clarification of the most important data quality aspects in an ERP context is provided. Furthermore, some of the most important causalities between categories of data quality are defined.

Studies conducted on advanced planning and scheduling (APS) systems have found problems in the marginal or negative returns from APS systems when they are implemented in manufacturing planning and control processes. The purpose of this study is to examine what problems exist in the onward and upward phase of the APS system implementation and how the individual, technical and organizational (ITO) dimensions in the implementation phases influence the problems in the onward and upward phase.

Three different manufacturing companies using a supply chain planning module to support their tactical manufacturing planning processes were chosen and their APS system implementation phases were studied. Interviews with the project members and the end-users, and on-site visits, were conducted. Internal company data and presentations were collected and analyzed according to four implementation phases and the ITO dimensions.

Three types of problems were identified in the onward and upward phase: process-related problems concerning difficulties to move forward; dependency on a consultancy firm; and too much time spent in the system. System-related problems include the usage of parallel systems and inadequate usage of the appropriate potential of the APS system. Plan-related problems regard an incorrect production plan. Different relationships between the ITO dimensions in the implementation process and the problem type were proposed.

The relationships identified in this paper are of important knowledge for companies who are implementing, or are in the process of implementing, APS systems.

There has been little written about the implementation issues of APS systems. The practical use of APS systems in the tactical planning is also relatively low. It is not known what problems to expect and how the ITO dimensions influence the problems during implementation. The findings this paper discusses fill some of these gaps.

The purpose of this paper is to explore the major drivers behind the choice of centralising versus decentralising warehousing locations from a small‐ and medium‐sized enterprise (SME) perspective. Previous literature has investigated this solely from a large company perspective.

An in‐depth literature review was carried out and, in addition, a single case study was conducted in order to investigate the issue at hand. A Danish medium‐sized do‐it‐yourself (DIY) retailer was chosen, as this company faced the challenge of deciding between centralisation vs decentralisation of its warehousing structure.

The paper has two findings: existing literature does not deal with the difference between SMEs and large companies when speaking of centralised vs decentralised warehousing; and the difference between SMEs and large companies with regard to centralised vs decentralised warehousing lies in the fact that SMEs generally have scarcity in competences and fewer resources, have fewer advantages of economies of scale in a centralised setting, and, finally, have fewer management resources to carry out a centralisation project.

It is a limitation of this research that a statistical generalisation is not possible. Therefore, the findings in this paper might not be applicable for all SMEs.

When speaking of centralising vs decentralising warehousing, SMEs should be aware that different drivers are at play when compared with larger companies.

Research in supply chain management and logistics has not addressed the consequences of warehousing structure from an SME perspective.

Logistics and supply chain management (SCM) are broad disciplines in which many different, cross‐functional tasks are investigated. In Scandinavia, research in logistics and SCM experienced a significant boom during the 1990s; the steadily increasing interest in participation in the annual NOFOMA Nordic Logistics Conference and the steadily growing number of PhD students enrolled in the Scandinavian research environments emphasizing the study of logistics and SCM bear witness to this intensification. In addition, a great number of doctoral dissertations in this field are completed in Scandinavia, adding greatly to the existent store of knowledge concerning a wide range of logistics and SCM phenomena. However, to date, precious little effort has been devoted to providing an overview of these dissertations. This paper is designed to fill that void. To that end, 75 doctoral dissertations published from 1990 to 2001 are identified. The framework classifies the dissertations into a series of main themes indicative of the state of Nordic research in logistics and SCM. Suggestions for future research based on this survey are likewise provided.